All measured data are in principle errored and in many cases the measured data are not continuously available. In addition, the measured data are frequently dependent on environmental conditions. Furthermore, different sensors or sensor systems generally have different temporal acquisition rates, are not synchronized with other sensors or sensor systems and have a latency time between the measurement and the output of the measured values. Sensor errors or measurement errors or errored measured values can be subdivided into quasi-stationary components that are constant over a plurality of measurements, such as e.g. an offset, and stochastic components that are random from measurement to measurement, such as e.g. noise. Whereas the random components are in principle not deterministically correctable, quasi-stationary errors can generally be corrected provided that they are observable. Non-correctable, significant errors can normally be at least avoided provided that they are recognizable.
In the prior art, different sensor fusion methods are already known which are normally also suitable for correcting or filtering measured data from different sensors or sensor systems. Particularly in the automotive sector, special requirements must be taken into account since a multiplicity of different sensors monitor a common environmental situation or a motor vehicle state by means of different measuring principles and describe this environmental situation or this motor vehicle state by means of a multiplicity of different measured data. The greatest possible resilience to random interference and a recognition and compensation of systematic errors are thus required for a sensor fusion applicable in the automotive sector. Similarly, temporal influences on the measured data must be corrected and temporary outages or the unavailability of sensors must be bridged.
In this context, DE 10 2010 063 984 A1, which is incorporated by reference describes a sensor system comprising a plurality of sensor elements. The sensor elements are designed so that they at least partially monitor different primary measured quantities and at least partially use different measuring principles. At least one measured quantity is then derived from at least one primary measured quantity of one or more sensor elements. Furthermore, the sensor system comprises a signal processing device, an interface device and a plurality of functional devices. The sensor elements and all functional devices are connected to the signal processing device, wherein the signal processing device is designed so that it in each case comprises at least one of the following signal processing functions for at least one of the sensor elements or its output signals:                an error handling,        a filtering, and        a calculation or provision of a derived measured quantity.        
The signal processing device makes the signal processing functions available to the functional devices.
DE 10 2012 216 215 A1, which is incorporated by reference similarly describes a sensor system which comprises a plurality of sensor elements and a signal processing device. The signal processing device is designed so that it at least partially jointly evaluates the sensor signals of the sensor elements. The signal processing device is furthermore designed so that time information directly or indirectly comprising information relating to the time of the respective measurement is allocated in each case to the measured data of physical quantities, wherein the signal processing device takes account of this time information at least in the generation of a fusion data set in a fusion filter. For the generation of the fusion data set, measured data having either matching time information or, if no measured data with matching time information are present, a corresponding measured value is created with the required time information by means of interpolation. Furthermore, the fusion filter assumes that error values of the measured data change only negligibly over a defined time period.
However, the generic methods and sensor systems known in the prior art have disadvantages for a plurality of reasons. Thus, insofar as acausal methods are involved, they have no real-time capability, since they perform calculations on available data sets several times and in different sequences. Conversely, other methods provide a real-time capability, but only with a processing overhead that is unacceptably high in series production, since a backward calculation to the measurement time is carried out on receipt of a measured value which is naturally delayed in order to then perform a forward calculation once more to the actual time. Furthermore, methods of this type provide only a comparatively small gain in terms of the accuracy of the processed measured values. Other known methods in turn suffer from their latency time burden, since they always undertake a fusion of the acquired measured data only when the sensor with the longest delay time has transmitted its measured data. Since, for example, a conventional GPS receiver has a delay time of some 100 ms, this produces a corresponding latency of the system as a whole. Further weaknesses of the known methods are e.g. the often applied processing-requirement-reducing principles based on preconditions that are unsuitable for heterogeneous sensor measurements, such as, for example, the assumption that no measurements of other sensors are fused during the delay time.